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Phylogenetic clustering of wingbeat frequency and flight-associated morphometrics across insect orders
by Tercel, M.P.T.G., Veronesi, F. and Pope, T.W.
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DOI: https://doi.org/10.1111/phen.12240
Tercel, M.P.T.G., Veronesi, F. and Pope, T.W. 2018. Phylogenetic clustering of wingbeat frequency and flight‐associated morphometrics across insect orders. Physiological Entomology.
24 March 2018 1 Title
2 Phylogenetic clustering of wingbeat frequency and flight-associated morphometrics across
3 insect orders
4
5 Running title
6 Flight strategies between insect orders
7
8 Authors
9 Maximillian P. T. G. Tercel*, Fabio Veronesi and Tom W. Pope
10
11 Affiliation
12 Department of Crop and Environment Science, Harper Adams University, Newport,
13 Shropshire, TF10 8NB, UK.
14
15 *Author for correspondence ([email protected])
16
17
18 KEY WORDS: Comparative morphology, wing loading, body mass, flight strategy, insect
19 evolution, high-speed filming.
20
21
22 23 Summary statement
24 Insect flight strategy varies between orders but is generally well conserved within orders, this
25 has important evolutionary and ecological implications at high taxonomic levels.
26
27 ABSTRACT
28 Wingbeat frequency in insects is an important variable in aerodynamic and energetic
29 analyses of insect flight and has been studied previously on a family- or species-level basis.
30 Meta-analyses of these studies have found order-level patterns that suggests flight strategy
31 is moderately well conserved phylogenetically. Studies incorporated into these meta-
32 analyses, however, use variable methodologies across different temperatures that may
33 confound results and phylogenetic patterns. Here, a high-speed camera was used to
34 measure wingbeat frequency in a wide variety of species (n = 102) in controlled conditions to
35 determine the validity of previous meta-analyses that show phylogenetic clustering of flight
36 strategy and to identify new evolutionary patterns between wingbeat frequency, body mass,
37 wing area, wing length, and wing loading at the order level. All flight-associated
38 morphometrics significantly affected wingbeat frequency. Linear models show that wing area
39 explained the most amount of variation in wingbeat frequency (R2 = 0.59, p = <0.001), whilst
40 body mass explained the least (R2 = 0.09, p = <0.01). A multiple regression model
41 incorporating both body mass and wing area was the best overall predictor of wingbeat
42 frequency (R2 = 0.84, p = <0.001). Order-level phylogenetic patterns across relationships
43 were consistent with previous studies. Thus, the present study provides experimental
44 validation of previous meta-analyses and provides new insights into phylogenetically
45 conserved flight strategies across insect orders.
46
47 48 INTRODUCTION
49 Wingbeat frequency in insects varies with body mass and wing area within and between
50 species (Byrne et al., 1988; Dudley, 2000), from 5.5 Hz in the helicopter damselfly
51 Megaloprepus caerulatus (Rüppell and Fincke, 1989) to over 1000 Hz in a ceratopogonid
52 Forcipomyia sp. midge (Sotavalta, 1953). How frequently an insect beats its wings is an
53 important variable when considering the biomechanics and physiology of insect flight
54 (Ellington, 1984a-f; Dudley, 2000; Alexander, 2002; Vogel, 2013). For any given body mass,
55 variables such as wing length, wing area, wing loading (body mass/wing area), wingbeat
56 frequency and stroke amplitude can differ substantially and affect the energetics and
57 biomechanics of insect flight, which is usually linked to evolutionary history (Byrne et al., 1988).
58 Stroke amplitude, the angle between the points of wing reversal, has been shown to vary
59 between taxa, from 66o in syrphids (Ellington, 1984c) to 180o in beetles (Atkins, 1960) and
60 moths (Wilkins, 1991) and may vary significantly during a single flight as shown in dragonflies
61 (Alexander, 1986), orchid bees (Dudley, 1995; Dillon and Dudley, 2004), and fruit flies
62 (Lehmann and Dickinson, 1998; Fry et al., 2003). Though undeniably important to
63 understanding insect flight strategy and aerodynamics, stroke amplitude was not measured in
64 the current study. This is because although both wingbeat frequency and stroke amplitude
65 change during a single flight, wingbeat frequency is kept relatively constant because of the
66 high energetic cost of deviating from the resonant frequency of the flight apparatus (Dudley,
67 2000). Conversely, stroke amplitude may be altered extremely rapidly to change direction (Fry
68 et al., 2003) or flight mode e.g. from hovering to forward flight (Dillon and Dudley, 2004).
69 Because of this variability, stroke amplitude is likely to be a slightly less reliable indicator of
70 flight strategy than wingbeat frequency.
71 The variables that influence the energetic and biomechanical aspects of flight could be used
72 to broadly characterize flight strategies between different orders of insects. Typically, higher
73 wingbeat frequencies are associated with insects of smaller size, to overcome the increasingly
74 viscous forces of the air present at small spatial scales, represented by low Reynolds numbers 75 Re in the order of 10-100 in the smallest insects (Ellington, 1999; Wang, 2005), and to better
76 control their direction in a windswept world (Vogel, 2013). Furthermore, frequencies of >100
77 Hz are facilitated by asynchronous, or myogenic, flight muscle present in endopterygote
78 (Coleoptera, Diptera, Hymenoptera) and exopterygote (Thysanoptera and Hemiptera) groups
79 (Dudley, 2000) where one nerve impulse can initiate several wingbeats through stretch-
80 activation caused by mechanical loading on the wing (Pringle, 1967). Thus, the highest
81 wingbeat frequencies are found in smaller members of these groups (Byrne et al., 1988).
82 Members from other orders possess large wings that they beat at lower frequencies relative
83 to other insects of comparable body mass e.g. Lepidoptera and Neuroptera (Dudley, 2000)
84 and Orthoptera (Snelling et al., 2012, 2017). Larger wings can produce more force per beat
85 than smaller wings, and therefore fewer beats are needed per unit time. Moreover, larger
86 wings afford lower wing loadings for insects of the same body mass, so wingbeat frequency
87 may be reduced further. It is possible then that flight-associated morphometrics, such as wing
88 area, can be used to predict wingbeat frequency and characterize flight for different groups of
89 insect using the same stroke strategy (i.e. conventional wingbeat or clap-fling).
90
91 Flight morphology and wingbeat frequency are dependent on the aerodynamic needs of the
92 insect according to their ecological niche and oxygen consumption increases with wingbeat
93 frequency (Bartholomew and Casey, 1978). Species with similar wing loadings may have
94 different wingbeat frequencies based on the flight velocity required to fulfil their ecological role.
95 Substantial variation in wingbeat frequency and flight morphology as a product of ecological
96 needs also exists within orders, such as the differences between Sphingidae and Nymphalidae
97 (Lepidoptera), where sphingids have small wings, rapid beat frequencies and very fast flight,
98 whilst nymphalids have much larger wings and lower wingbeat frequencies, usually flying at
99 overall slower speeds (Dudley, 2000). Such variation could conceal relationships between
100 flight-associated morphometrics and wingbeat frequency across higher taxonomic levels,
101 decreasing the overall level of phylogenetic grouping of flight strategy. 102
103 Order-level taxonomic relationships to these flight-associated morphometrics have been
104 studied before (see Byrne et al., 1988; Dudley, 2000) but meta-analyses suffer from
105 differences in both ambient conditions and methods of measuring wingbeat frequency
106 between studies that may confuse relationships. For example, acoustic methods,
107 stroboscopes, and high-speed cameras were used across studies incorporated into Dudley
108 (2000) and Byrne et al.’s (1988) meta-analyses. Chadwick (1939) suggested stroboscopic
109 methods are difficult to use effectively to glean kinematic data in insects because of the slight
110 variations in wingbeat frequency and movements of the specimen during testing, making
111 visualisation of the wing at the frequency of the strobe light challenging and Unwin and
112 Ellington (1979) suggested picking up acoustic signals of smaller species difficult even with
113 highly sensitive microphones. Both stroboscopic (e.g. Chen et al., 2014) and acoustic (e.g.
114 Raman et al., 2007) methods have, however, been used successfully to measure wingbeat
115 frequency in insects since advancement in the quality of measurement instruments (i,e, optical
116 tachometers and microphones). Nevertheless, stroboscopic/optical and acoustic methods are
117 not absolute measures of wingbeat frequency. High-speed cameras, in contrast, allow the
118 recording of a temporally magnified visual depiction of the motion of insect wings. The
119 reliability of the methods used in studies incorporated into important meta-analyses varies
120 because of the problems faced when the technology was less well developed. Furthermore,
121 temperatures vary from 7-25oC between studies used in previous meta-analyses. Insect
122 wingbeat frequency has been shown to increase with higher temperatures (Unwin and Corbet,
123 1984; Oertli, 1989) and, therefore, meta-analyses of the relationships between measured
124 characteristics may be confounded. An experimental approach using high-speed cameras in
125 controlled conditions recording flight in species across several orders has not previously been
126 done. Using common UK species of insect, relationships between body mass, wing length,
127 wing area, wing loading and wingbeat frequency were investigated to determine if flight
128 strategies could be broadly characterized between different orders of insect. 129
130 MATERIALS AND METHODS
131 Study specimens
132 Adult insects were caught using either sweep net (EFE & GB Nets, Totnes, Devon, UK –
133 handle length = 0.3 m; net diameter = 0.5 m; net depth = 0.7 m), pooter (NHBS, Totnes, Devon,
134 UK – barrel diameter = 30 mm, length = 55 mm, suction tube diameter = 5mm), or hand
135 collected into small sampling pots (varying sizes) within a 20 km radius of Harper Adams
136 University, Shropshire, UK (latitude ~52.772°N, longitude ~2.411°W) over the course of June
137 and July, 2017. In total, 112 specimens across 102 species in 10 orders were used in the
138 analysis.
139
140 Filming area and conditions
141 Filming took place inside a Fitotron® Standard Growth Room unit (Weiss Technik, Ebbw Vale,
142 UK) set to a constant 20oC and 60% relative humidity. This temperature was selected to film
143 flight behaviour of insects in standardised conditions and is unlikely to represent an extreme
144 for tested species, which were all collected during summer days and therefore active within
145 ~±5oC of the ambient temperature used. Ambient lighting intensity was 280 μmol m-2 s-1 inside
146 the Fitotron® unit and no other external light source was used. A flight box made of 6
147 transparent Perspex® panels, measuring 30x30x30 cm once constructed, was used to contain
148 flights of the specimens whilst filming. Study specimens were introduced to the flight box either
149 via a 2.5 cm diameter aperture made in the centre of one of the panels by offering up an open
150 test tube containing a specimen, or, for larger specimens, the entire panel could be removed
151 and the specimen introduced.
152
153 Filming procedure 154 Each specimen was filmed 2-5 times using an FPS1000HD monochromatic high-speed
155 camera (The Slow Motion Camera Company, London, UK). Specimens were filmed each time
156 during free flight. For each flight recorded, the camera was handheld in order to track insects
157 in free flight. This helped increase total length of each video and thus more reliably count
158 wingbeats. Across videos, insects were filmed from various angles, but this did not affect video
159 analysis. Sufficient video footage was gathered in <10 minutes for each specimen.
160
161 Morphological measurements
162 Specimens were killed in a killing jar (a jar with a base of plaster of Paris to which ethyl acetate
163 was intermittently added when needed) after the last video was recorded and immediately
164 weighed using a precision balance (Cahn C-33 Microbalance, Cerritos, California, USA). The
165 functional wing (in insects with only one pair of functional wings e.g. Diptera and Coleoptera)
166 or wing couple on the right side (i.e. the fore- and hindwing on the right side of the insect
167 viewed dorsally) was removed by dissection under a stereo microscope and forewing length
168 (henceforth wing length) was measured using a pair of digital calipers (0.01 mm precision),
169 measured from the base of the forewing to the most distal tip. A photo was taken of the
170 dissected wing couple using a microscope camera making sure the wings were perpendicular
171 to the camera lens. Wing area was measured in ImageJ version 1.49 (Schindelin et al., 2012)
172 by using the photo and following the ImageJ process for measuring leaf area (Reinking, 2007)
173 as in previous studies on insect wings (e.g. Outomuro et al., 2013); the wing area value was
174 multiplied by 2 to quantify total wing area assuming symmetry. Wing loading was determined
175 by dividing body mass by total wing area.
176
177 Video analysis
178 Videos were first converted into a viewable format using ImageJ, where video frames-per-
179 second (FPS) was then altered to allow individual wingbeats to be clearly visible. A wingbeat 180 was judged to be both a full downstroke and full upstroke, terminating at pronation before the
181 next wingbeat (Fig. 1), and in all groups except for Odonata, fore- and hindwings beat at the
182 same time. For odonates, forewing and hindwing pairs were measured separately then the
183 mean was calculated; the difference between the wing pairs did not exceed 2 beats in any of
184 the odonate specimens. Sections of videos were carefully selected to represent free-flight,
185 omitting wingbeats immediately after take-off until a more regular rhythm was observed, which
186 was usually more rapid. The number of wingbeats nv during free-flight was counted for each
187 video. Equation 1 was used to determine the wingbeat frequency n (Hz) from each video
188 where tv is the length of the video in seconds, and fm is the multiplication factor (the factor that
189 describes by how much time is magnified in each video), which is calculated by dividing filming
190 FPS by video playback FPS. All species were filmed at 1000 FPS except for 6 species of
191 nematoceran Diptera, which were filmed at 2000 FPS.
192
193 Statistical Analysis
194 Statistical analysis was conducted using R version 3.4.1. “Single Candle” (R Core Team,
195 2017) with packages MASS (Venebles and Ripley, 2002), ggplot2 (Wickham, 2009), caret
196 (Kuhn, 2017), hydroGOF (Zambrano-Bigiarini, 2014), relaimpo (Grömping, 2006), and
197 gridExtra (Auguie, 2016) used. Both simple and multiple linear regression analyses were
198 conducted to determine the relationships between morphological variables and wingbeat
199 frequency. Data were log-transformed to reduce skew and allow analysis by linear regression.
200 To better measure the level of phylogenetic clustering of flight strategy, a principal component
201 analysis (PCA) was conducted.
202
203 RESULTS
204 Morphometric data 205 Table 1 compiles the range and mean statistics for morphological measurements and
206 wingbeat frequency in each sampled order. Across all 112 specimens, wingbeat frequency
207 covered a range between 12.468 to 557.351 Hz ( ̅ = 121.588, sd = 92.679, se = 8.767), body
208 mass a range of 0.0003 to 2.245 g ( ̅ = 0.097, sd = 0.256, se = 0.024), wing length a range of
209 0.172 to 5.214 cm ( ̅ = 1.184, sd = 0.919, se = 0.087), wing area a range of 0.022 to 23.362
210 cm2 ( ̅= 2.022, sd = 4.088, se = 0.386), and wing loading a range of 0.0028 to 0.245 g/cm2
211 ( ̅= 0.061, sd = 0.059, se = 0.006).
212
213 These values show that some orders were better sampled than others and in some cases this
214 is reflected in the ranges of different variables recorded. Average values, however, are
215 generally in agreement with expected values for UK insects. Synchronous fliers
216 (Ephemeroptera, Lepidoptera, Mecoptera, Neuroptera, Odonata, Trichoptera) were overall
217 less well sampled than asynchronous fliers (Coleoptera, Diptera, Hemiptera, Hymenoptera)
218 and should be similarly taken into account when considering ranges of variables.
219
220 Relationships between morphometrics and wingbeat frequency
221 Figure 2 shows the relevant linear relationships between the log10 transformed morphometric
222 data. Of these, wing area (cm2) was the best predictor of wingbeat frequency (R2 = 0.59, p =
223 <0.001). The strongest overall linear relationship between all morphometric measurements
224 was between wing length (cm) and wing area (R2 = 0.93, p = <0.001). Body mass explained
225 only 9% of the variation in wingbeat frequency across specimens (R2 = 0.09, p = <0.01) and
226 represented the poorest predictor of wingbeat frequency across the measured morphometrics.
227 Taxonomic distribution on the graphs (Fig. 2, especially A-D) sees a diffuse but identifiable
228 clustering of the orders most intensively sampled, suggesting that orders may broadly adhere
229 to a specific strategy and some new phylogenetic clustering between wingbeat frequency,
230 wing area, wing length, and wing loading have been revealed where previous meta-analyses 231 focussed solely on taxonomic grouping in relation to wingbeat frequency and body mass. For
232 example, looking at Figure 2D, Hymenoptera are quite closely clustered at the higher end of
233 the wing loading range and the upper-middle range of wingbeat frequency, denoting that most
234 hymenopterans sampled have small wings relative to their body mass, which they beat at
235 above average frequencies compared to other orders.
236
237 A multiple regression model using log10 values of wing area (β = -0.034, p = <0.001) and body
238 mass (β = 0.001, p = <0.001), with a fit of R2 = 0.84 was the best overall model predicting
239 wingbeat frequency in insects: wingbeat frequency = (wing area * -0.77) + (body mass * 0.37)
240 + 5.56.
241
242 A dominance analysis (Azen and Budescu, 2003; Grӧmping, 2006) was conducted to
243 determine the relative importance of the explanatory variables to the response variable in the
244 model and showed that body mass and wing area explained 17.3% and 67.2% of the change
245 in wingbeat frequency, respectively.
246
247 Phylogenetic clustering of flight strategy
248 Wingbeat frequency and morphometric variables for all specimens were reduced to a dataset
249 summarising the variance and covariance between each using a Principle Component
250 Analysis (PCA). Initial eigenvalues indicated the first two principle components explained
251 64.039% and 33.291% of the data, respectively (97.331% cumulatively). Dimension 1 is
252 mainly loaded towards wing area (30.417%), wing length (30.073%), body mass (22.882%),
253 and wing loading (16.388%), whereas Dimension 2 is mainly loaded towards wing loading
254 (58.325%), wingbeat frequency (24.979%), and body mass (15.821%). Having determined the
255 loadings, a PCA biplot was produced to view the relationship between variables and whether 256 insect orders were clustered on the graph. Figure 3 reveals that most insect specimens are in
257 close proximity to their associated centroid (the mean value of the x and y coordinates for
258 each order), shown by the ellipses, which represent one standard deviation along each axis
259 and is rotated toward the direction of maximum spread of the point cloud. This strongly
260 suggests that flight strategy is well conserved at the order level.
261
262 DISCUSSION
263 Phylogenetic clustering apparent in this study broadly agrees with results from previous meta-
264 analyses (Byrne et al., 1988; Dudley, 2000). Past research looking at differences in wingbeat
265 frequency and flight-associated morphometrics are, therefore, experimentally validated by the
266 present study through the use of high-speed filming. However, although all measured
267 characteristics significantly affected wingbeat frequency, body mass did not show as clear a
268 relationship to it as in previous meta-analysis (Dudley, 2000). This is likely because of the lack
269 of specimen variation in the present study, compared to the very high number of different
270 specimens across a much broader body mass range in the meta-analysis (Dudley, 2000).
271 Indeed, previous meta-analyses included species from a much wider geographical range,
272 incorporating studies from many different countries and ecosystems, including those from
273 tropical forests.
274
275 Wing length and wing area are both able to predict wingbeat frequency moderately accurately,
276 explaining 42% and 59% of its variation, respectively. Wing length may affect wingbeat
277 frequency as a product of increasing body mass, where larger insects have slightly longer
278 wings to offset the lower wingbeat frequency and maintain good advance ratios (Vogel, 2013),
279 though this is also connected to wing area (Fig. 2E). Area of the wing generally increases with
280 body mass to accommodate the greater level of lift generation required and longer wings tend
281 to have a greater area than shorter ones. Thus, an increased area means fewer beats are 282 necessary per unit time to generate the same amount of lift. This is supported by the positive
283 relationship between wing loading and wingbeat frequency, where heavily loaded wings are
284 generally beaten more rapidly to generate enough lift. Relatively heavily loaded wings must
285 keep a weight aloft with a reduced area and are associated with larger insects (Fig. 2F)
286 because wing area, proportional to the square of body length, cannot keep pace with body
287 mass, proportional to the cube of body length, as insect size increases (Bartholomew and
288 Heinrich, 1973; Byrne et al., 1988; Ennos, 1989; Dudley, 2000; Vogel, 2013). Despite this,
289 heavier insects tended to also have lower wingbeat frequencies (Fig. 2C). Whilst initially
290 paradoxical that heavier insects with greater wing loading beat their wings relatively less
291 frequently, this is because smaller insects must overcome the increasingly viscous forces of
292 air at small scales, greater relative drag, and the greater effect of the wind on their direction
293 by beating their wings comparatively faster (Dudley, 2000; Alexander, 2002; Vogel, 2013) and
294 because the oscillatory frequency of the thorax is inversely dependent on its size, which
295 directly influences wingbeat frequency in asynchronous fliers (Pringle, 1949, 1967; Dickinson
296 and Tu, 1997; Dudley, 2000).
297
298 The best overall model explaining the variation in wingbeat frequency incorporated body mass
299 and wing area, the relative importances of which were 17.3% and 67.2%, respectively. This
300 suggests that despite the weak linear relationship between body mass and wingbeat
301 frequency, together with wing area the variables can explain 84% of the variation in wingbeat
302 frequency. These findings support previous agreement (Jensen, 1956; Ellington, 1984b-c,
303 1999; Dudley, 1990, 2000; Alexander, 2002) that wingbeat frequency is in large part
304 dependent on wing area and body mass.
305
306 Palaeopterous insects using direct flight muscles and neopterous insects using synchronous
307 flight muscles show generally lower wingbeat frequencies than insects with asynchronous 308 flight muscles (Figure 2) and these two groups are further clustered in Figure 3 (Neuroptera,
309 Lepidoptera, Odonata – bottom right; asynchronous fliers – middle/top left). The weak
310 relationship between wingbeat frequency and body mass in the present study as well as past
311 meta-analyses may arise because of the differences in scaling between these groups. Insects
312 with indirect synchronous flight muscles conduct wingbeats by single nerve impulses to the
313 tergosternal (wing depressor) and dorsal-longitudinal (wing elevator) muscles. Thus, the
314 wingbeat frequency of insects with synchronous musculature is determined by the frequency
315 of nervous stimulation to the muscles. In contrast, insects that possess asynchronous muscles
316 have essentially random nervous stimulation relative to the wingbeat frequency (Dickinson
317 and Tu, 1997). Wingbeat frequency in asynchronous fliers is determined primarily by the
318 resonant features of the pterothoracic apparatus to maximise efficiency of energy expenditure
319 (Pringle, 1949; Dickinson and Tu, 1997), as well as behavioural changes during rapid
320 manoeuvring (Nachtigall and Wilson, 1967). Asynchronous muscles are stretch-activated
321 (Pringle, 1949, 1967) by their antagonistic pair within the pterothorax and are therefore
322 dependent on mechanical loading. The inertial load of the whole thorax-wing system must
323 increase with body mass and wingbeat frequency has been shown to vary inversely with wing
324 inertia (Sotavalta, 1952). For asynchronous fliers, scaling of the resonant flight apparatus is
325 therefore especially important, as the oscillatory frequency of the pterothorax is inversely
326 dependent on its size, which directly influences wingbeat frequency (Pringle, 1949, 1967;
327 Dickinson and Tu, 1997; Dudley, 2000). In synchronous fliers, wing amputation experiments
328 to lower wing inertia results in only a relatively small increase in wingbeat frequency in
329 Periplaneta cockroaches and Agrontia moths compared to asynchronous fliers (Roeder,
330 1951), suggesting wingbeat frequency in synchronous fliers is independent of mechanical
331 load. Thus, asynchronous fliers are more likely to show a stronger scaling relationship
332 between wingbeat frequency and body mass than other insects. No strong inferences relating
333 to scaling differences between synchronous and asynchronous fliers can be made in the
334 present study because Lepidoptera encompassed the only well sampled synchronous fliers. 335
336 Orders are shown to be clustered when wingbeat frequency is viewed as a function of one of
337 the other measured morphometrics (Fig. 2A-C), supporting the idea that flight strategy can be
338 generally characterized based on evolutionary history. This may be because of a combination
339 of several factors: 1) species inherit a flight apparatus that can only be changed to a certain
340 extent in a given time to fit a new role/niche e.g. Coleoptera inherit heavy elytra, one pair of
341 functional wings, asynchronous flight muscles, and low flight muscle mass ratio relative to
342 body mass (Marden, 1987; Dudley, 2000) making it unlikely for them to be able to fill the role
343 of an aerial predator but well adapted to infrequent spells of sustained flight; 2) species may
344 need to fly in the same way even though they have different ecological niches, which may
345 increase the level of intra-order clustering because the existing flight apparatus can be used
346 to fulfil the same aerodynamic needs despite interacting with different organisms e.g.
347 Syrphidae and Tabanidae need to fly in similar ways – visiting flowers vs. visiting vertebrate
348 hosts (female tabanids), ability to hover above resources, ability to change direction rapidly to
349 regularly escape predators or swatting etc.; and 3) a specific goal may be achieved in more
350 than one way e.g. Diptera: Asilidae and Odonata are both aerial predators with a high
351 proportion of relative flight muscle mass (Marden, 1987), but likely utilise completely different
352 flight strategies because of their very different inherited flight apparatuses. Combined, these
353 factors suggest that although an inherited flight apparatus is predisposed to certain flight
354 strategies and precludes others, it can be somewhat modified in some instances to fit new
355 ecological niches or maintained if aerodynamic needs do not change with differing ecological
356 interactions. Ultimately, this may improve levels of flight strategy conservation at the order
357 level.
358
359 Order-level flight strategies may have interesting energetic, ecological, and evolutionary
360 implications though intra-order exceptions exist where some groups fly in unconventional
361 ways. For example, flies are very light to medium weight with high wingbeat frequencies, 362 medium to low wing area and wing length, and medium to high wing loading (Fig. 2A-D). These
363 attributes afford flies the ability to fly quickly, perform complex aerobatic manoeuvres and to
364 hover, conferring obvious ecological advantages to certain groups. Mosquitos and
365 chironomids, however, possess wingbeat frequencies that are unusually high, and wing
366 loadings that are unusually low relative to other Diptera (Table S1, Supplementary Information)
367 that likely increases energetic costs of flight substantially, and may be used for acoustic
368 communication during swarming and mating (Neems et al., 1992; Takken et al., 2006;
369 Bomphrey et al., 2017). One potential explanation of this presumably highly energetically
370 expensive trait uncharacteristic of most other members of the order may be related to sexual
371 selection, where males and females “duet” by reaching a common harmonic tone based on
372 their usually different wingbeat frequencies (Cator et al., 2009; Robert, 2009; Bomphrey et al.,
373 2017).
374
375 The variation between different clades within orders suggests broad categorization is possible,
376 with infrequent exceptions. For most orders, however, relationships between wingbeat
377 frequency and flight-associated morphometrics show moderately well conserved patterns
378 across the graphs. These align with previous meta-analyses (Byrne et al., 1988; Dudley, 2000)
379 looking at wingbeat frequency in relation to body mass, with the same orders covering the
380 same areas on the graphs (see Fig. 3.3B in Dudley, 2000). The present study therefore
381 provides strong experimental evidence that flight strategy is broadly conserved at the order
382 level, as specimens are generally clustered phylogenetically, and this validates previous meta-
383 analyses investigating wingbeat frequency and flight-associated morphometrics, although
384 there is evidence that some flight strategies show similarity between certain groups. The PCA
385 analysis could though be improved by incorporating other variables, such as relative flight
386 muscle mass, which is shown to be important when considering the ecology of different orders
387 (Marden, 1987; Dudley, 2000).
388 389 Energetic and ecological costs and benefits of differing flight behaviours are still poorly known
390 in most insect groups, though some have received attention e.g. Hymenoptera: Apidae:
391 Euglossini (see Casey et al., 1985; Dudley, 1995; Dillon and Dudley, 2004), Lepidoptera:
392 Sphingidae and Saturniidae (Bartholomew and Casey, 1978), Orthoptera: Acrididae (Snelling
393 et al., 2012), and Hymenoptera: Apidae: Bombini (Ellington et al., 1990). Elucidation of the
394 ecological pressures leading to adaptation of specific flight strategies and the energetic costs
395 associated may help illuminate evolutionary trade-offs. These trade-offs are likely to explain
396 the phylogenetic clustering found across flight-associated morphometrics and wingbeat
397 frequency in the present study. Studies that combine quantitative evaluation of insect flight
398 energetics with additional qualitative comparisons between orders can go some way in
399 revealing why different groups utilise different flight strategies (e.g. between bees, moths, and
400 locusts in Snelling et al., 2012). Further work to reveal ecological pressures and energetic
401 costs of broad flight strategies in different orders is therefore required to infer why insect
402 groups fly the way they do.
403
404 ACKNOWLEDGEMENTS
405 The authors would like to thank Danielle Klassen, George Hicks, and Todd Jenkins for
406 technical assistance during flight filming and field work and Aidan Thomas, Joe Roberts, and
407 Todd Jenkins for providing some of the specimens. George Hicks and Todd Jenkins for their
408 assistance with species taxonomy.
409
410 COMPETING INTERESTS
411 The authors declare no competing or financial interests.
412
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537
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552 553 Figure 1. Images a-k show a complete wingbeat in the beetle Rutpela maculata
554 (Coleoptera: Cerambycidae); t is time in milliseconds from the start of the wingbeat. a. the
555 end of pronation; b-e. downstroke translation; e-g. supination; h-j. upstroke translation; j-k.
556 pronation.
557
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573 574 Figure 2. Relationships between log10 transformed morphometric variables. a, wingbeat
575 frequency (Hz) as a function of wing length (cm): wingbeat frequency = -0.764 * wing length +
576 4.479, R2 = 0.42, p = <0.001; b, wingbeat frequency as a function of wing area (cm2): wingbeat
577 frequency = -0.413 * wing area + 4.345, R2 = 0.59, p = <0.001; c, wingbeat frequency as a
578 function of body mass (g): wingbeat frequency = -0.129 * body mass + 4.04, R2 = 0.09, p =
579 <0.01; d, wingbeat frequency as a function of wing loading (g/cm2): wingbeat frequency =
580 0.385 * wing loading + 5.799, R2 = 0.29, p = <0.001; e, wing area as a function of wing length:
581 wing area = 2.105 * wing length – 0.307, R2 = 0.93, p = <0.001; f, wing loading as a function
582 of body mass: wing loading = 0.356 * body mass – 1.977, R2 = 0.34, p = <0.001; g, wing area
583 as a function of body mass: wing area = 0.644 * body mass + 1.977, R2 = 0.63, p = <0.001.
584
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595 596 Figure 3. Principle component data for Dimensions 1 and 2, categorised into different
597 insect orders by symbol shape and colour. Small translucent symbols represent
598 specimens and large opaque symbols represent the centroids for each order. The ellipses
599 around each centroid represent one standard deviation along each axis of the associated
600 order and are rotated in the direction of maximum spread. Trichoptera, Ephemeroptera, and
601 Mecoptera lack ellipses because of an insufficient sample size. The Dimension scores show
602 a moderate-high level of clustering of orders in relation to measured variables, as specimens
603 are generally in close proximity to their associated centroid. The black point in the top-right
604 quarter of the graph is the mean direction of the arrows and suggests the variables are on
605 average positively correlated with dimensions 1 and 2.
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617
618 619 Table 1. Range and mean of wingbeat frequency and associated morphological
620 measurements in each sampled order. Number of species are denoted in parentheses beside
621 sample size in the right-most column.
622
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639 640 Supplementary Information
641 Table S1. Wingbeat frequency and morphological measurements of all specimens. Lists
642 specimens by body mass in ascending order. Cells with a “-“ denote the specimen failed to
643 be identified to the associated taxonomic rank.
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661 662 Figure 1.
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675 676 Figure 2.
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696 697 Table 1.
Wingbeat frequency (mean Number of ` Bodymass (g) Wing length (cm) Wing area (cm2) Wing loading (g/cm2) Hz) specimens range mean range mean range mean range mean range mean Coleoptera 79 - 123.396 97.512 0.0061 - 0.117 0.0539 0.521 - 1.188 0.898 0.19 - 0.982 0.545 0.0321 - 0.141 0.085 10(10)
Diptera 59.567 - 557.351 208.244 0.0005 - 0.162 0.0268 0.172 - 1.739 0.729 0.022 - 1.17 0.327 0.0119 - 0.168 0.0554 28(28)
Ephemeroptera n/a 75.0454 n/a 0.0027 n/a 0.634 n/a 0.306 n/a 0.00882 1(1)
Hemiptera 90.222 - 152.247 116.39 0.0011 - 0.14 0.0226 0.345 - 1.185 0.624 0.112 - 1.186 0.445 0.009 - 0.118 0.034 11(11)
Hymenoptera 87.129 - 230.987 163.89 0.0024 - 0.223 0.103 0.356 - 1.48 1.006 0.038 - 1.234 0.64 0.022 - 0.245 0.136 24(15)
Lepidoptera 12.468 - 64.566 39.606 0.0044 - 2.24 0.203 0.646 - 5.214 1.792 0.318 - 23.362 5.031 0.004 - 0.096 0.025 22(22)
Mecoptera n/a 48.885 n/a 0.0398 n/a 1.387 n/a 1.492 n/a 0.027 1(1)
Neuroptera 25-923 - 94.413 52.801 0.0003 - 0.0065 0.0035 0.352 - 1.393 0.757 0.106 - 1.972 0.701 0.003 - 0.007 0.005 6(6)
Odonata 17.847 - 40.665 32.331 0.0278 - 1.23 0.27 1.795 - 5.158 3.002 1.964 - 22.784 8.768 0.0112 - 0.054 0.022 8(6)
Trichoptera n/a 27.515 n/a 0.159 n/a 2.267 n/a 4.738 n/a 0.0336 1(1) 698
699
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709
710 711 Supplementary Table.
Wingbeat frequency Bodymass Wing length Wing area Wing loading Species Genus Family Order (mean Hz) (g) (cm) (cm2) (g/cm2)
- Micromus Hemerobiidae Neuroptera 94.413 0.0003 0.352 0.106 0.003
- Syrphus. Syrphidae Diptera 190.860 0.0005 0.172 0.022 0.023
- - Psychodidae Diptera 144.611 0.0006 0.267 0.048 0.013
- - - Diptera 204.355 0.0009 0.252 0.050 0.018
- - Chironomidae Diptera 557.351 0.0011 0.406 0.064 0.017
- - Miridae Hemiptera 127.872 0.0011 0.345 0.122 0.009
Thaumatomyia notata Thaumatomyia Chloropidae Diptera 269.741 0.0014 0.234 0.038 0.037
- - - Hemiptera 152.247 0.0014 0.358 0.114 0.012
Uroleucon cirsii Uroleucon Aphididae Hemiptera 99.603 0.0015 0.353 0.112 0.013
- - Chironomidae Diptera 544.494 0.0018 0.423 0.070 0.026
- - Tipulidae Diptera 94.606 0.0020 0.687 0.168 0.012
Hemerobius humulinus Hemerobius Hemerobiidae Neuroptera 46.583 0.0021 0.586 0.336 0.006
- Torymus Torymidae Hymenoptera 160.011 0.0024 0.360 0.098 0.024
Wesmaelius subnebulosis Wesmaelius Hemerobiidae Neuroptera 45.304 0.0026 0.640 0.416 0.006
Centroptilum luteolum Centroptilum Baetidae Ephemeroptera 75.045 0.0027 0.634 0.306 0.009
- - Braconidae Hymenoptera 136.261 0.0029 0.438 0.134 0.022
- - Chloropidae Diptera 180.050 0.0030 0.342 0.070 0.043
- - Braconidae Hymenoptera 164.443 0.0030 0.356 0.038 0.079
- Wesmaelius Hemerobiidae Neuroptera 54.583 0.0033 0.666 0.444 0.007
- - - Diptera 195.996 0.0039 0.354 0.098 0.040 - - Syrphidae Diptera 198.890 0.0044 0.403 0.124 0.035
Pseudargyrotoza conwagana Pseudargyrotoza Tortricidae Lepidoptera 64.246 0.0044 0.653 0.318 0.014
- - Miridae Hemiptera 120.832 0.0048 0.530 0.250 0.019
Culex pipiens Culex Culicidae Diptera 334.037 0.0049 0.578 0.158 0.031
- - Tortricidae Lepidoptera 52.214 0.0055 0.785 0.612 0.009
- - Crambidae Lepidoptera 57.948 0.0059 0.799 0.562 0.010
Micromus angulatus Micromus Hemerobiidae Neuroptera 50.000 0.0059 0.904 0.932 0.006
Oulema melanopus Oulema Chrysomelidae Coleoptera 123.398 0.0061 0.521 0.190 0.032
Chrysoperla carnea Chrysoperla Chrysopidae Neuroptera 25.923 0.0065 1.393 1.972 0.003
Aedes cantans Aedes Culicidae Diptera 286.949 0.0066 0.627 0.182 0.036
Culiseta annulata Culiseta Culicidae Diptera 344.160 0.0070 0.572 0.150 0.047
Macrolophus sp. Macrolophus Miridae Hemiptera 139.717 0.0076 0.515 0.244 0.031
Lobesia abscisana Lobesia Tortricidae Lepidoptera 64.566 0.0076 0.646 0.526 0.014
Culiseta annulata Culiseta Culicidae Diptera 331.157 0.0077 0.602 0.174 0.044
- - - Hemiptera 116.865 0.0104 0.619 0.490 0.021
Propylea 14-punctata Propylea Coccinellidae Coleoptera 102.427 0.0105 0.589 0.210 0.050
Pasiphila rectangulata Pasiphila Geometridae Lepidoptera 41.358 0.0107 0.938 1.132 0.009
Pterophorus pentadactyla Pterophorus Pterophoridae Lepidoptera 32.333 0.0114 1.192 1.120 0.010
Nephrotoma flavescens Nephrotoma Tipulidae Diptera 79.470 0.0118 1.015 0.402 0.029
- - Miridae Hemiptera 108.171 0.0119 0.710 0.402 0.030
Pandemis cerasana Pandemis Tortricidae Lepidoptera 54.184 0.0124 0.835 0.890 0.014
Athalia scuttelariae Athalia Tenthredinidae Hymenoptera 87.129 0.0132 0.826 0.352 0.038
Xanthorhoe montanata Xanthorhoe Geometridae Lepidoptera 29.243 0.0133 1.561 2.980 0.004
Lygus rugulipennis Lygus Miridae Hemiptera 115.183 0.0140 0.574 0.298 0.047 Nephrotoma quadrifaria Nephrotoma Tipulidae Diptera 67.360 0.0181 1.084 0.464 0.039
Rhagonycha fulva Rhagonycha Catharidae Coleoptera 79.712 0.0183 0.653 0.380 0.048
Chloromyia formosa Chloromyia Stratiomyidae Diptera 156.043 0.0183 0.736 0.312 0.059
Haematopota pluvialis Haematopota Tabanidae Diptera 151.568 0.0183 0.779 0.302 0.061
- - - Hemiptera 112.917 0.0184 0.682 0.518 0.036
- - Vespidae: Eumeninae Hymenoptera 135.597 0.0184 0.703 0.292 0.063
- - Empididae Diptera 151.321 0.0193 0.759 0.288 0.067
Oedemera nobilis Oedemera Oedemeridae Coleoptera 112.656 0.0210 0.698 0.232 0.091
Scathophaga stercoraria Scathophaga Scathophagidae Diptera 104.015 0.0224 0.854 0.366 0.061
- - Ichneumonidae Hymenoptera 110.116 0.0233 0.942 0.546 0.043
Manulea lurideola Manulea Erebidae Lepidoptera 33.095 0.0258 1.470 2.542 0.010
Syrphus ribesii Syrphus Syrphidae Diptera 177.908 0.0273 0.994 0.512 0.053
Coenagrion puella Coenagrion Coenagrionidae Odonata 37.495 0.0277 1.795 1.964 0.014
Harmonia axyridis Harmonia Coccinellidae Coleoptera 79.000 0.0283 0.993 0.644 0.044
Anania hortulata Anania Crambidae Lepidoptera 40.996 0.0293 1.448 2.410 0.012
Episyrphus balteatus Episyrphus Syrphidae Diptera 166.057 0.0294 1.025 0.488 0.060
Idaea aversata Idaea Geometridae Lepidoptera 32.088 0.0303 1.471 2.420 0.013
Coenagrion puella Coenagrion Coenagrionidae Odonata 36.691 0.0316 1.984 2.072 0.015
- Aphodius Scarabaeidae Coleoptera 93.054 0.0327 0.987 0.560 0.058
Aphantopus hyperantus Aphantopus Nymphalidae Lepidoptera 16.014 0.0373 2.168 7.262 0.005
Coenagrion puella Coenagrion Coenagrionidae Odonata 36.839 0.0374 1.902 2.190 0.017
- Andrena Apidae Hymenoptera 213.815 0.0376 0.703 0.352 0.107
- - - Hemiptera 90.222 0.0383 0.989 1.164 0.033
- - Syrphidae Diptera 208.540 0.0385 0.872 0.340 0.113 Panorpa communis Panorpa Panorpidae Mecoptera 48.885 0.0398 1.387 1.492 0.027
- Andrena Apidae Hymenoptera 172.581 0.0453 0.690 0.346 0.131
- Sarcophaga Sarcophagidae Diptera 149.643 0.0540 0.995 0.526 0.103
Calliphora vomitoria Calliphora Calliforidae Diptera 214.835 0.0549 0.874 0.460 0.119
Hypena proboscidalis Hypena Noctuidae Lepidoptera 30.587 0.0565 1.137 4.496 0.013
- Tipula Tipulidae Diptera 59.567 0.0676 1.739 1.170 0.058
Pieris brassicae Pieris Pieridae Lepidoptera 12.468 0.0691 2.593 10.992 0.006
Vespula germanica Vespula Vespidae Hymenoptera 145.156 0.0769 1.126 0.628 0.122
Ectemnius cavifrons Ectemnius Crabronidae Hymenoptera 210.688 0.0800 1.037 0.542 0.148
- Zygaena Zygaenidae Lepidoptera 60.595 0.0804 1.669 2.640 0.030
Vespula germanica Vespula Vespidae Hymenoptera 152.006 0.0818 1.061 0.610 0.134
Vespula germanica Vespula Vespidae Hymenoptera 146.908 0.0833 0.530 0.536 0.155
Vespula vulgaris Vespula Vespidae Hymenoptera 173.277 0.0874 1.081 0.598 0.146
Apis mellifera Apis Apidae Hymenoptera 230.987 0.0886 0.995 0.588 0.151
- Aphodius Scarabaeidae Coleoptera 103.159 0.0929 1.018 0.658 0.141
Rutpela maculata Rutpela Cerambicidae Coleoptera 86.840 0.1026 1.188 0.768 0.134
Geometra papilionaria Geometra Geometridae Lepidoptera 22.023 0.1071 2.632 10.194 0.011
Chrysoteuchia culmella Chrysoteuchia Crambidae Lepidoptera 40.626 0.1090 1.041 1.330 0.082
Calopteryx splendens Calopteryx Calopterygidae Odonata 19.318 0.1092 3.054 9.760 0.011
- Aphodius Scarabaeidae Coleoptera 101.111 0.1095 1.161 0.822 0.133
Polygonia c-album Polygonia Nymphalidae Lepidoptera 27.501 0.1145 2.298 7.696 0.015
Bombus pascuorum Bombus Apidae Hymenoptera 198.274 0.1166 1.074 0.614 0.190
Leptura quadrifasciata Leptura Cerambicidae Coleoptera 93.768 0.1173 1.173 0.982 0.119
Orthosia gothica Orthosia Noctuidae Lepidoptera 47.053 0.1253 1.753 3.260 0.038 Pentatoma rufipes Pentatoma Pentatomidae Hemiptera 96.667 0.1397 1.185 1.186 0.118
Calopteryx virgo Calopteryx Calopterygidae Odonata 17.847 0.1457 3.287 10.466 0.014
Bombus terrestris ♀ Bombus Apidae Hymenoptera 183.029 0.1504 1.171 0.818 0.184
Bombus lapidarius Bombus Apidae Hymenoptera 199.547 0.1536 1.114 0.626 0.245
Phryganea grandis Phryganea Phryganeidae Trichoptera 27.515 0.1590 2.267 4.738 0.034
Sympetrum striolatum Sympetrum Libellulidae Odonata 40.665 0.1595 2.894 6.944 0.023
Volucella pellucens Volucella Syrphidae Diptera 134.179 0.1613 1.418 1.152 0.140
Volucella bombylans Volucella Syrphidae Diptera 133.078 0.1624 1.337 0.966 0.168
Bombus terrestris ♀ Bombus Apidae Hymenoptera 186.521 0.1641 1.229 0.864 0.190
Bombus terrestris ♀ Bombus Apidae Hymenoptera 152.625 0.1854 1.436 1.048 0.177
Bombus terrestris ♀ Bombus Apidae Hymenoptera 153.182 0.1972 1.422 1.128 0.175
Bombus terrestris ♀ Bombus Apidae Hymenoptera 144.712 0.2081 1.454 1.134 0.184
Bombus terrestris ♀ Bombus Apidae Hymenoptera 161.815 0.2125 1.425 1.086 0.196
Bombus terrestris ♂ Bombus Apidae Hymenoptera 165.085 0.2154 1.480 1.152 0.187
Bombus terrestris ♂ Bombus Apidae Hymenoptera 149.597 0.2227 1.480 1.234 0.180
Orthetrum cancellatum Orthetrum Libellulidae Odonata 38.577 0.4176 3.944 13.960 0.030
Deilephila elpenor Deilephila Sphingidae Lepidoptera 53.715 0.5281 3.026 6.792 0.078
Laothoe populi Laothoe Sphingidae Lepidoptera 29.330 0.8449 4.085 17.152 0.049
Aeshna grandis Aeshna Aeshnidae Odonata 31.214 1.2296 5.158 22.784 0.054
Acherontia atropos Acherontia Sphingidae Lepidoptera 29.160 2.2403 5.214 23.362 0.096
712
713
714 715 Equation 1.